Parameters Of Robot Research Articles

Multi-objective optimization design method for the dimensions and control parameters of curling hexapod robot based on application performance

The curling hexapod robot is different from previous curling robots, which can mimic the throwing actions of curlers. To ensure its performance, the size and control parameters of the robot need to be optimized. However, there is still a lack of parameter design optimization methods for hexapod curling robots based on application performance and considering the coupling effects of size and control parameters. To meet this demand, this paper combines Simscape Multibody simulation modeling and genetic algorithm to propose a multi-objective optimization method for the dimensions and control parameters of curling hexapod robot based on application performance. This method uses Simscape Multibody to establish a parametric model of the curling robot, and proposes an optimization search method using genetic algorithms based on constraints and objective functions. Finally, the dimensions and control parameters of the curling robot that meet the application performance requirements are solved through the optimization method.

An analysis and reliability-based optimization design method of trajectory accuracy for industrial robots considering parametric uncertainties

To address the challenges of poor trajectory accuracy in industrial robots, which has emerged as a technological bottleneck hindering further robots’ applications in high-precision manufacturing industries, this paper proposes a method for the analysis and reliability-based optimization design for industrial robots’ trajectory accuracy considering parametric uncertainties. Firstly, the dynamic equation of an articulated industrial robot with six degrees of freedom is derived, incorporating the Stribeck joint friction model, followed by the uncertain parameter identification of this dynamic model. Subsequently, an uncertainty simulation system for the robot is established based on the constructed dynamic model and the sensitivity of system uncertain parameters to the robot trajectory accuracy is analyzed, where 10 key parameters are obtained among 54 uncertain parameters. Finally, a reliability-based multi-objective optimization design methodology is proposed synthesizing the robot trajectory accuracy, manufacturing cost, and quality loss, to achieve tolerance design of the robot's parameters, and enables minimizing costs and quality losses while ensuring the robot's trajectory accuracy reliability. The performance and practicality of the proposed method were validated using a six-degree-of-freedom rotary joint serial industrial robot as an example.

Automatic setting optimization for robotic upper-extremity rehabilitation in patients with stroke using ReoGo-J: a cross-sectional clinical trial

Stroke-induced upper-extremity paralysis affects a substantial portion of the population, necessitating effective rehabilitation strategies. This study aimed to develop an automated program, incorporating the item response theory, for rehabilitation in patients with post-stroke upper-extremity paralysis, focusing on the ReoGo-J device, and to identify suitable robot parameters for robotic rehabilitation. ReoGo-J, a training device for upper-extremity disorders including 71 items, was administered to over 300 patients with varying degrees of post-stroke upper-extremity paralysis. Each item was rated on a three-point scale (0, very difficult; 1, adequate; 2, very easy). The results were analyzed using the graded response model, an extension of the two-parameter logistic model within the framework of the item response theory, to grade the training items based on ability of the patients. The relationship between the predicted ability, an indicator of the predicted ability the paralyzed upper-extremity to perform an item in the item response theory analysis (higher numbers indicate higher ability, lower numbers indicate lower ability), and the items in the Fugl–Meyer assessment (FMA), which indicate the degree of paralysis, was analyzed using Pearson’s correlation coefficient. This study included 312 patients with post-stroke upper-extremity paralysis. The predicted ability (θ) of the tasks included in the original ReoGo-J test for forward reaching, reaching for mouth, rotational reaching, radial reaching (2D), abduction reaching, reaching in eight directions, radial reach (3D), and reaching for head ranged from − 2.0 to − 0.8, − 1.3 to − 0.8, − 1.0 to − 0.1, − 0.7 to 0.3, − 0.2 to 0.4, − 0.4 to 0.6, − 0.1 to 0.6, and 0.5 to 0.6, respectively. Significantly high correlations (r = 0.80) were observed between the predicted ability of all patients and the upper-extremity items of shoulder-elbow-forearm in the FMA. We have introduced an automated program based on item response theory and determined the order of difficulty of the 71 training items in ReoGo-J. The strong correlation between the predicted ability and the shoulder-elbow-forearm items in FMA may be used to ameliorate post-stroke upper-extremity paralysis. Notably, the program allows for estimation of appropriate ReoGo-J tasks, enhancing clinical efficiency.Trial registration: https://www.umin.ac.jp/ctr/index-j.htm (UMIN000040127).

Identification of physically consistent dynamics parameter of the ABB IRB 360-6/1600 delta robot and its use for time-optimal motion planning under consideration of constraint forces

Model-based control schemes, forward dynamics simulations, constraint force computation and time-optimal motion planning have one major thing in common, they all depend on the dynamics parameters of the system. Physical consistency of the dynamics parameters ensures a positive definite mass matrix and correct constraint forces. The most common inverse dynamics identification method – the base-parameters – lack physical consistency. This paper proposes an identification method to identify physically consistent dynamics parameters for Delta-like robots while further showing the effects of friction in passive joints. A tailored model to compute the crucial constraint forces appearing in the mechanism based on the identified dynamics parameters is derived. This model is used to additionally consider constraint forces besides actuation torques for time-optimal motion planning of a typical pick and place task. This is done without any prior CAD data of the robot from the manufacturer.

The Dynamics Stability Judging Method for a Gantry-type Welding Robot

In order to improve the dynamic performance of gantry-type welding robot, this paper introduces the structural dynamics model and Lyapunov stability criterion of the gantry-type welding robot frame with nine degrees of freedom. The dynamic equations of the welding robot satisfy the Euler–Lagrange form, and the calculation index of welding robot Lyapunov stability is judged by matrix eigenvalues. Combining with the specific design parameters of an industrial welding robot, the motion stability of the gantry-type welding robot frame and the displacement response of the welding torch are analyzed. The influence of the change of the frame design structure on the stability of the welding torch for end effector of welding robot is discussed, furthermore, the improvement direction of vibration damping and anti-interference of welding robot system is pointed out. Finally, the dynamics simulation of the optimized gantry-type welding robot end effector is carried out by using ADAMS software, and the torque variation curve of the end effector is obtained, which provides a strong basis for the selection of motor models and the design of control system.

Personalizing Human–Robot Workplace Parameters in Human-Centered Manufacturing

This study investigates the relationship between collaborative robot (CR) parameters and worker utilization and system performance in human–robot collaboration (HRC) environments. We investigated whether optimized parameters increase workplace efficiency and whether adapting these parameters to the individual worker improves workplace outcomes. Three experimental scenarios with different CR parameters were analyzed in terms of the setup time, assembly time, finished products, work in process, and worker utilization. The main results show that personalized CR parameters significantly improve efficiency and productivity. The scenario in which CR parameters were tailored to individual workers, balanced the workload, and minimized worker stress, resulting in higher productivity compared to non-people-centric settings. The study shows that personalization reduces cognitive and physical stress, promotes worker well-being, and is consistent with the principles of human-centered manufacturing. Overall, our research supports the adoption of personalized, collaborative workplace parameters, supported by the mathematical model, to optimize employee efficiency and health, contributing to human-centered and efficient HRC environments.

Repetitive Impedance Learning-Based Physically Human-Robot Interactive Control.

Model-based impedance learning control can provide variable impedance regulation for robots through online impedance learning without interaction force sensing. However, the existing related results only guarantee the closed-loop control systems to be uniformly ultimately bounded (UUB) and require the human impedance profiles being periodic, iteration-dependent, or slowly varying. In this article, a repetitive impedance learning control approach is proposed for physical human-robot interaction (PHRI) in repetitive tasks. The proposed control is composed of a proportional-differential (PD) control term, an adaptive control term, and a repetitive impedance learning term. Differential adaptation with projection modification is designed for estimating robotic parameters uncertainties in the time domain, while fully saturated repetitive learning is proposed for estimating time-varying human impedance uncertainties in the iterative domain. Uniform convergence of tracking errors is guaranteed by the PD control and the use of projection and full saturation in the uncertainties estimation and is theoretically proved based on a Lyapunov-like analysis. In impedance profiles, the stiffness and damping are composed of an iteration-independent term and an iteration- dependent disturbance, which are estimated by repetitive learning and compressed by the PD control, respectively. Therefore, the developed approach can be applied to the PHRI where iteration-dependent disturbances exist in the stiffness and damping. The control effectiveness and advantages are validated by simulations on a parallel robot in a repetitive following task.

Accurate relative measurement of multitarget poses by monocular vision for nonmodel-based real-time calibration of industrial robot

On basis of multitarget poses measured by monocular vision, a nonmodel-based real-time calibration method for six-DoF industrial robot is presented. Markers fixed on base and joints of robot are measured by a camera synchronously, at each measurement configuration selected with constraints of marker visibility and work trajectory, to provide decoupled pose data. The accuracy problem due to low precision of camera is addressed by substituting absolute measurement of movable markers with relative measurement from a static marker to the movable ones. On this basis, the deviated pose of each joint can be obtained accurately and the actual kinematic parameters of robot are obtained. The simulation verifies the selection algorithm of measurement configurations and evaluates the effects of decisive error sources on measurement accuracy. The experiment demonstrates the advantage of relative pose measurement using the low-cost camera on calibration accuracy by comparison with typical model-based calibrations using a precise laser tracker.

Two-step calibration of 6-DOF industrial robots by grouping kinematic parameters based on distance constraints

The positioning accuracy of industrial robots is critical for their applications, which can be improved through calibration. This paper proposes a two-step calibration method for six-degree-of-freedom (6-DOF) industrial robots to reduce couplings of their modified Denavit-Hartenberg (MD-H) model parameters during optimization processes of kinematic calibration. In this method, a distance error model is established based on the distance constraints, and the MD-H model parameters of the industrial robots are identified group by group using the Levenberg-Marquardt (LM) algorithm. To determine the optimal MD-H model parameter grouping, the different grouping strategies are simulated and analyzed. In the proposed method, the coordinate system transformation between the measurement frame and the robot base frame can be avoided based on the distance error model, thereby reducing the errors introduced by the coordinate system transformation during the calibration process. Finally, the proposed two-step calibration method is verified through an experiment. The experimental result shows that the distance/positioning accuracy on the robot end-effector is improved from 0.602 mm/2.7317 mm to 0.151 mm/0.717 mm after calibration. The presented study may be beneficial for improving performance of industrial robots.

Identification Algorithm for Stability Improvement of Welding Robot End-Effector

Aiming to solve the problem that the significant error between the actual joint torque and the calculated joint torque of a welding robot leads to the vibration of the end-effector, which in turn affects the stability of the end-effector, this paper proposes a identification algorithm based on the Weighted Least Squares Genetic Algorithm (WLS-GA) to construct and solve the dynamical model to obtain the accurate dynamical parameters. Firstly, a linear model of welding robot dynamics is derived. The fifth-order optimal Fourier series excitation trajectory is designed to collect experimental data such as joint torque. Then, a rough solution of the parameters to be recognized is obtained by solving the dynamics model through the Weighted Least Squares (WLS) method, the search space is determined based on the rough solution, and the optimal solution is obtained by using the Genetic Algorithm (GA) to perform a quadratic search in the search space. Finally, the identification data obtained from the algorithm is analyzed and compared with the experimental data. The results show that the error between the identification data obtained using the WLS-GA identification algorithm and the experimental data is relatively small. The results show that the identification data obtained using the WLS-GA identification algorithm have less error than the experimental data, taking the Root Mean Square (RMS) value of the joint torque error obtained using the weighted least squares algorithm as a criterion. The accuracy of the WLS-GA identification algorithm can be improved by up to 66.85% compared with that of the weighted least squares algorithm and by up to 78.0% compared with that of the Ordinary Least Squares (OLS) algorithm. In summary, the WLS-GA identification algorithm can accurately identify the dynamic parameters of the welding robot and more accurately construct a dynamic model to solve the effect of joint torque error on the control characteristics of the welding robot. It can improve the stability of the end-effector of the welding robot to ensure the quality of the automobile body and beam welding and welding speed.

Knowledge-based self-tuning of PID control gains for continuum soft robots

In recent years, a class of soft (deformable) robots has attracted the attention of researchers due to their continuum features being advantageous over rigid robots for dexterous and safe compliant motions. These distinct advantages are obtained from the elastomer hyperelastic material properties because continuum robots are manufactured and actuated through embedded pneumatic energy fields. In this way, the continuum deformation raises novel problems in modeling and control due to its highly nonlinear behavior. That is, the deformable shape of its components varies continuously, unlike rigid robots, suggesting that the controller should compensate for its varying continuum model. Motivated by the deformable soft robot subject to parametric uncertainties and unmodeled dynamics, this paper proposes an adaptive, instead of constant, tuning of feedback gains of a model-free controller. The tuning mechanism is based on a knowledge-based scheme using a discrete wavelet neural network (DWN) by an efficient input–output identification updating its parameters and weights online, with a gradient descent algorithm driven by the identification error. At the same time, the control feedback gains are self-tuned, minimizing a convex cost function of tracking errors. Dynamic simulations show the performance of the proposed approach considering the parameters of a real soft robot mimicking a gelatin-like behavior. Finally, some discussions on the proposed scheme’s computational cost, feasibility, and viability are briefly addressed.

Bi-Objective Function Optimization for Welding Robot Parameters to Improve Manipulability

Bi-Objective Function Optimization for Welding Robot Parameters to Improve Manipulability

Simultaneous identification of geometric parameters and structural stiffness of serial robots composing flexible links

It has been a formidable challenge to accurately predict the pose of serial robots composing flexible links due to the difficulties in simultaneously considering geometric errors and link flexibility. This paper presents an approach for simultaneously identifying kinematics and stiffness parameters in the flexible serial robot. A compliance equivalent method is employed to model the flexibility in the joints and links nonlinearly. Based on this, the kinetostatics model of those serial robots can be established analytically. By approximating the force-deflection characteristics of the flexible links, a unified error model containing both geometric and compliance parameters is derived. Moreover, an adaptive total-least-square method is proposed for the identification considering constraints on the compliance coefficient. To verify the effectiveness of the proposed method, simulations on a single flexible link, a serial robot, and experiments on a 3-DOF planar serial manipulator are carried out. The results show that the residual errors of the manipulator can be significantly reduced by identifying the kinematics and stiffness parameters using the proposed approach.

Design of composite puncture blood collection system and research on puncture force.

Venous blood collection testing is one of the most commonly used medical diagnostic methods. Compared with conventional venous blood collection, robotic collection can reduce needle-stick injuries, medical staff workload, and infection risk; allow doctor-patient isolation; and improve collection reliability. Existing venous blood collection robots use rigid puncture needles, which can easily puncture the lower wall of blood vessels, causing vessel damage and collection failure. This paper proposes a bionic blood collection strategy based on a composite puncture needle that mimics the structure and function of mosquito mouthparts. A bionic composite puncture needle insertion system with puncture-force sensing was designed, and venipuncture forces were simulated and mathematically modelled. A prototype insertion system was built and used in an experiment, which demonstrated effective composite puncture blood collection and explored the factors influencing puncture force. Puncture force decreases with increased puncture speed and angle and with a decreased needle diameter. This provides a basis for optimising the parameters of blood collection robots.

Application of Object Contrast for Forming Images Used in Technical Vision Systems for Navigation of Mobile Robots

The purpose of the article is to substantiate the feasibility of using object contrast as an informative feature for the formation of images used in technical vision systems. This goal is achieved by studying the dependence of the contrast of objects in images on the viewing geometry and determining the conditions under which the greatest similarity of the compared images is ensured. The solution to the first problem is based on the presentation of reference information about sighted objects, taking into account the navigation parameters of mobile robots. By modeling in the MATLAB software environment for typical viewing conditions, selective images were obtained using randomly selected fragments from Google Earth Pro, the distribution of contrast values and the cross-correlation function of the original and selective images. The influence of viewing angles on the distribution of contrasts and the formation of the decisive function was determined. The studies were performed for viewing angles of -60˚, -80˚ and -90˚ for altitudes in the range from 500 to 600 meters. The most significant result is a model of a set of reference images, taking into account the influence of navigation parameters on the contrast of objects, as well as experimentally established dependences of the distribution of contrasts for typical viewing conditions. The novelty of the work lies in the fact that the procedure for generating images and the decision function using the contrast of objects has been further developed. This will significantly increase the efficiency of selection of objects with insignificant brightness characteristics.

An improved iterative approach with a comprehensive friction model for identifying dynamic parameters of collaborative robots

AbstractCollaborative robots are becoming intelligent assistants of human in industrial settings and daily lives. Dynamic model identification is an active topic for collaborative robots because it can provide effective ways to achieve precise control, fast collision detection and smooth lead-through programming. In this research, an improved iterative approach with a comprehensive friction model for dynamic model identification is proposed for collaborative robots when the joint velocity, temperature and load torque effects are considered. Experiments are conducted on the AUBO I5 collaborative robots. Two other existing identification algorithms are adopted to make comparison with the proposed approach. It is verified that the average error of the proposed I-IRLS algorithm is reduced by over 14% than that of the classical IRLS algorithm. The proposed I-IRLS method can be widely used in various application scenarios of collaborative robots.

A machine learning-based motion training approach applied to multilegged and bipedal robots

To promptly gain an understanding of disasters as they occur and to draft plans for search and rescue operations, various types of robots are used. Robots not only increase rescue efficiency but also reduce firefighter casualties. Therefore, this study investigated how bionic robots can be utilized to search destroyed and chaotic disaster sites as quickly as possible. However, manually adjusting the motion parameters of robots performing robot motions is extremely inefficient. To resolve this problem, this study used machine learning algorithms to allow robots to train themselves in the environment and autonomously determine their optimal motion parameters. However, many types of machine learning algorithms exist, each with their own strengths and weaknesses. Therefore, this study designed a series of experiments to investigate the features of each algorithm in optimizing the robots’ motions; subsequently, this study compared the strengths and weaknesses of each algorithm based on their performance. The results indicated that for both multipedal and bipedal robots, the use of machine learning to find the optimal motion parameters is both feasible and practical.

Adaptive fractional-order integral fast terminal sliding mode and fault-tolerant control of dual-arm robots

AbstractClosed-loop kinematics of a dual-arm robot (DAR) often induces motion conflict. Control formulation is increasingly difficult in face of actuator failures. This article presents a new approach for fault-tolerant control of DARs based on advanced sliding mode control. A comprehensive fractional-order model is proposed taking nonlinear viscous and viscoelastic friction at the joints into account. Using integral fast terminal sliding mode control and fractional calculus, we develop two robust controllers for robots subject to motor faults, parametric uncertainties, and disturbances. Their merits rest with their strong robustness, speedy finite-time convergence, shortened reaching phase, and flexible selection of derivative orders. To avoid the need for full knowledge of faults, robot parameters, and disturbances, two versions of the proposed approach, namely adaptive integral fractional-order fast terminal sliding mode control, are developed. Here, an adaptation mechanism is equipped for estimating a common representative of individual uncertainties. Simulation and experiment are provided along with an extensive comparison with existing approaches. The results demonstrate the superiority of the proposed control technique. The robot performs well the tasks with better responses (e.g., with settling time reduced by at least 16%).

Research on force/position switching control of servo actuator for hydraulically driven joint robot

According to the robot’s walking motion characteristics, the position/force switching control is studied to realize the segmental control of the robot stroke. This stroke is controlled by position when the foot end of the robot descends from the suspension to the ground. To avoid excessive contact force when the robot touches the ground, force control is carried out when the foot touches the ground. Due to the force and position control methods and control parameters of the hydraulic quadruped robots are different, the precise mathematical model for the joint position control and joint force control of the leg joints of the hydraulic quadruped robot is established using the system identification method. A fuzzy multi-model switching algorithm is proposed to solve the problem of jumping and jitter of system parameters in the process of force/position switching. Through simulation and prototype experiments, fuzzy multi-model switching is compared with direct switching and multi-model switching, and the switching effect of the algorithm is verified.

Region optimization and sensitivity analysis of THT assembly process parameters based on flexible soldering robot platform

In order to solve the problem that the selection of key process parameters of a flexible soldering robot depends on manual experience, the empirical model of its THT assembly process parameters is established with the response surface method and the reliability of the model is verified. On this basis, the multi-parameter relative sensitivity of the key process parameters is analyzed through the Monte-Carlo simulation, and their single-parameter sensitivity curve is drawn. Then, based on the sensitivity analysis, the stability region division method is designed, and the stability region of process parameters for soldering quality comprehensive score is obtained. The optimal region of process parameters of the soldering robot are acquired by compressing the stability region: soldering temperature from 350-368 ℃, soldering time from 1.31-1.48 s and solder feeding volume from 1.5-2.0 mm3. The average value of the soldering quality comprehensive score within the optimal region of process parameters is 90.2 points.